Encoder.
Our encoder is a series of Transformer blocks [1]} with an input embedding layer and a positional encoding layer.
The encoder only operates on unmasked patches.
As the semantics of time series are more straightforward than languages, we use four layers of Transformer blocks, far less than the depth of Transformer-based models in computer vision [2]}, [3]} and natural languages [4]}, [5]}.
Specifically, the input embedding layer is a linear projection to transform the unmasked patches into latent space:
\(\mathbf {U}_j^i = \mathbf {W}\cdot \mathbf {S}^i_j + \mathbf {b},\) 
